Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction Surface Sampling Probe

The discovery of novel bioactive compounds remains a cornerstone of natural product (NP) chemistry. However, traditional NP discovery workflows are time- and resource-intensive, hindering sustainability and efficiency of multicondition screening projects. This study evaluates the application of the liquid microjunction surface sampling probe (LMJ-SSP) with partial least-squares discriminant analysis (PLS-DA) as a preliminary step in the NP discovery pipeline. By integrating ambient mass spectrometry with machine learning, we analyzed four strains of Penicillium fungi grown under 13 unique conditions in situ, without sample preparation. Using PLS-DA, we prioritized the growth conditions that maximized chemical diversity, offering insights into metabolite composition prior to resource-intensive steps in the NP discovery workflow. This LMJ-SSP-based approach achieved a significant reduction in sampling time (96%), overall cost (98%), and solvent consumption (98%)─streamlining the NP discovery pipeline through chemically informed prioritization and improved sustainability.